Theory in the short story "Sogavanam"

Damage to the environment affects not only humans, but all living beings. People get tensed when something harms their beloved and the same people don’t get tensed when birds and animals are affected. Knowingly or unknowingly, animals and birds play an important role in human living environment. A wise man knowingly destroys nature. S. Tharman's short story “Sogavanam” shows that the birds that nest in trees and stay in tree trunks and roam around happily are greatly affected by the urbanization. He has created a sad forest with his writings and mentioned about the noisy environment due to the increase in traffic and the change in the food styles of the birds. The story travels along with the ecological aspects of conservation and non-pollution of ecological factors. This article examines how ecological principles fit into narrative events and how the future society should maintain ecological factors

A comparative study of intravenous dexmedetomidine and midazolam on prolongation of spinal anesthesia

Background: The present study was conducted to compare the efficacy and safety of intravenous dexmedetomidine and midazolam on prolongation of spinal anesthesia.Methods: The study population included people who were undergoing for spinal anesthesia for various surgeries. A total of 90 subject were randomized equally to Dexmedetomidine, Midazolam and saline groups using a computer generated random number sequence. Three study groups were compared with respect to all the baseline variables. The key outcome parameters and hemodynamic parameters were compared among the three study groups.Results: No statistically significant differences were observed in baseline paramters across study groups. The median values of patient satisfaction score and anesthesiologist satisfaction score were almost equal among three study groups, but the association was statistically not significant. The median VAS and the median HSL were slightly lower in dexmedetomidine group than other two groups (VAS-1,2,3 respectively and HSL -4,6,6 respectively) with statistically significant association (P0.05) except with number of patients requiring analgesic for the first 24 hours (P value<0.05).Conclusions: Measurement of patient and anesthesiologist satisfaction scores are more or less similar in midazolam and dexmedetomidine groups compare to saline group

SCREENING FOR PHYTOCHEMICALS AND FTIR ANALYSIS OF MYRISTICA DACTYLOIDS FRUIT EXTRACTS

Objective: The present investigation focus on screening of phytochemicals and FT-IR analysis of Myristica dactyloids fruit extracts. The fruit extracts were prepared using five different solvents.Methods: The phytochemical analysis and FT-IR (Fourier transform infrared spectroscopy) analysis were performed using standard methods.Results: The results reveals that the alkaloids, steroids, flavonoids, phenolic compounds, proteins, carbohydrates, cardio glycosides and saponins were present in methanolic extract when compared to other solvent extracts. FT-IR analysis shows the presence of different functional groups such as carboxylic acids, aromatics, alkanes, alcohols, phenols, aliphatic amines, alkenes and amine groups in the fruit extracts.Conclusion: The study concluded that the methanolic extract (M. dactyloides fruit) has potential bioactive compounds

Tree Based Boosting Algorithm to Tackle the Overfitting in Healthcare Data

Healthcare data refers to information about an individual's or population's health issues, reproductive results, causes of mortality, and quality of life. When people interact with healthcare systems, a variety of health data is collected and used. However, these healthcare data are noisy as well as it prone to over-fitting. Over-fitting is a modeling error in statistics that occurs when a function is too closely aligned to a limited set of data points. As a result, the model learns the information and noise in the training data to the point where it degrades the model's performance on fresh data. The tree-based boosting approach works well on over-fitted data and is well suited for healthcare data. Improved Paloboost performs trimmed gradient and updated learning rate using Out-of-Bag mistakes collected from Out-of-Bag data. Out-of-Bag data are the data that are not present in In-Bag data. Improved Paloboost's outcome will protect against over-fitting in noisy healthcare data and outperform all tree baseline models. The Improved Paloboost is better at avoiding over-fitting of data and is less sensitive, according to experimental results on health-care datasets

A study to assess the awareness and knowledge about the maternal nutrition and complications encountered by the antenatal mothers in the rural population

Background: Nutrition plays a vital role in life. Good nutrition is an important part of leading a healthy lifestyle. A healthy pregnancy diet will promote your baby's growth and development. The purpose of this study was to highlight the knowledge, attitude and practices of pregnant women regarding the healthy diet, psychological support, regular visits, danger signs and complications during pregnancy among mothers who visit our hospital along with different socio demographic factors.Methods: This study was conducted on 350 antenatal women from January 2016 to February 2017 at Tamil Nadu, India. A 24 point- 15 minutes’ questionnaire was designed about the knowledge, attitude and practice about the nutrition, danger signs and complications in pregnancy.Results: Around 98% of women were very clear that nutrition is necessary in pregnancy and 53% of them told that the quantity of food intake should be increased. Major source of knowledge about the nutrition was obtained from the family members (81%). The common danger sign was abdominal pain (61%) followed by bleeding per vaginum (22%). About 77% of mothers had an idea that minimum of 6-10 visit should be there in their antenatal period.Conclusions: This study emphasizes that health professionals should concentrate more in the nutritional values and the antenatal classes should be taken regarding the role of adequate nutrition, constituents and sources of balanced diet and the consequences of over and under nutrition

Prognóstico de exploração no Chat GPT com ética de inteligência artificial

Natural language processing innovations in the past few decades have made it feasible to synthesis and comprehend coherent text in a variety of ways, turning theoretical techniques into practical implementations. Both report summarizing software and sectors like content writers have been significantly impacted by the extensive Language-model. A huge language model, however, could show evidence of social prejudice, giving moral as well as environmental hazards from negligence, according to observations. Therefore, it is necessary to develop comprehensive guidelines for responsible LLM (Large Language Models). Despite the fact that numerous empirical investigations show that sophisticated large language models has very few ethical difficulties, there isn't a thorough investigation and consumers study of the legality of present large language model use. We use a qualitative study method on OpenAI's ChatGPT3 to solution-focus the real-world ethical risks in current large language models in order to further guide ongoing efforts on responsibly constructing ethical large language models. We carefully review ChatGPT3 from the four perspectives of bias and robustness. According to our stated opinions, we objectively benchmark ChatGPT3 on a number of sample datasets. In this work, it was found that a substantial fraction of principled problems are not solved by the current benchmarks; therefore new case examples were provided to support this. Additionally discussed were the importance of the findings regarding ChatGPT3's AI ethics, potential problems in the future, and helpful design considerations for big language models. This study may provide some guidance for future investigations into and mitigation of the ethical risks offered by technology in large Language Models applications.Las innovaciones en el procesamiento del lenguaje natural en las últimas décadas han hecho posible sintetizar y comprender textos coherentes en una variedad de formas, transformando las técnicas teóricas en implementaciones prácticas. Ambos informan que el software extenso y las industrias como la de los creadores de contenido se han visto significativamente afectadas por el modelo de lenguaje extensivo. Sin embargo, un modelo de lenguaje enorme podría mostrar evidencia de sesgo social, dando riesgos morales y ambientales por negligencia, según las observaciones. Por lo tanto, es necesario desarrollar lineamientos completos para los LLM (Modelos de Lenguaje Grandes) responsables. A pesar de que numerosas investigaciones empíricas muestran que los modelos sofisticados de lenguaje amplio tienen muy pocas dificultades éticas, no existe una investigación exhaustiva y un estudio del consumidor sobre la legalidad del uso actual de modelos de lenguaje amplio. Usamos un método de estudio cualitativo en ChatGPT3 de OpenAI para enfocarnos en resolver los riesgos éticos del mundo real en los modelos actuales de lenguaje amplio para guiar aún más los esfuerzos en curso en la construcción responsable de modelos éticos de lenguaje amplio. Analizamos cuidadosamente ChatGPT3 desde las cuatro perspectivas de sesgo y robustez. De acuerdo con nuestras opiniones expresadas, comparamos ChatGPT3 objetivamente en múltiples conjuntos de datos de muestra. En este trabajo se encontró que una fracción sustancial de los problemas de principios no son resueltos por los marcos actuales; por lo tanto, se han proporcionado nuevos ejemplos de casos para respaldar esto. Además, se discutió la importancia de los hallazgos sobre la ética de la IA de ChatGPT3, los problemas potenciales en el futuro y las consideraciones de diseño útiles para modelos de lenguaje grandes. Este estudio puede proporcionar algunas pautas para futuras investigaciones y mitigación de los riesgos éticos que ofrece la tecnología en grandes aplicaciones de Language Models.As inovações de processamento de linguagem natural nas últimas décadas tornaram possível sintetizar e compreender textos coerentes de várias maneiras, transformando técnicas teóricas em implementações práticas. Ambos relatam que softwares resumidos e setores como criadores de conteúdo foram significativamente afetados pelo extenso modelo de linguagem. Um enorme modelo de linguagem, no entanto, poderia mostrar evidências de preconceito social, dando riscos morais e ambientais por negligência, de acordo com as observações. Portanto, é necessário desenvolver diretrizes abrangentes para LLM (Large Language Models) responsáveis. Apesar do fato de numerosas investigações empíricas mostrarem que modelos sofisticados de linguagem ampla têm muito poucas dificuldades éticas, não há uma investigação completa e estudo de consumidores sobre a legalidade do uso atual de modelos de linguagem ampla. Usamos um método de estudo qualitativo no ChatGPT3 da OpenAI para focar na solução os riscos éticos do mundo real nos atuais modelos de linguagem ampla, a fim de orientar ainda mais os esforços contínuos na construção responsável de modelos éticos de linguagem ampla. Analisamos cuidadosamente o ChatGPT3 a partir das quatro perspectivas de viés e robustez. De acordo com nossas opiniões declaradas, comparamos objetivamente o ChatGPT3 em vários conjuntos de dados de amostra. Neste trabalho, constatou-se que uma fração substancial dos problemas de princípios não é resolvida pelos referenciais atuais; portanto, novos exemplos de casos foram fornecidos para apoiar isso. Além disso, foram discutidas a importância das descobertas sobre a ética de IA do ChatGPT3, possíveis problemas no futuro e considerações de design úteis para grandes modelos de linguagem. Este estudo pode fornecer algumas orientações para futuras investigações e mitigação dos riscos éticos oferecidos pela tecnologia em grandes aplicações de Modelos de Linguagem

Infants With Congenital Adrenal Hyperplasia Are at Risk for Hypercalcemia, Hypercalciuria, and Nephrocalcinosis

In a retrospective study, most young children with CAH had at least one episode of hypercalcemia, whereas a smaller percentage was found to have hypercalciuria and/or nephrocalcinosis